Projekt

Pobieranie bibliotek

library(dplyr)
library(ggplot2)
library(plotly)

Wczytywanie danych

data <- read.csv2('./all_summary.csv', nrows = 10000)
dim(data)

## [1] 10000   412

Usunięcie wybranych wierszy

deletable_res_name <- c("UNK", "UNX", "UNL", "DUM", "N", "BLOB", "ALA", "ARG", "ASN", "ASP", "CYS", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS", "MET", "MSE", "PHE", "PRO", "SEC", "SER", "THR", "TRP", "TYR", "VAL", "DA", "DG", "DT", "DC", "DU", "A", "G", "T", "C", "U", "HOH", "H20", "WAT")
data <- data[!data$res_name %in% deletable_res_name,]
dim(data)

## [1] 9940  412

Wyczyszczenie danych z instancji pustych

#data <- data[complete.cases(data), ]
#dim(data)

Podsumowanie danych

#knitr::kable(summary(data))
dim(data)

## [1] 9940  412

Najpopularniejsze

# finding 50 most popular res_names
popular_res_names <- data %>% group_by(res_name) %>% summarise(n = n()) %>% arrange(-n) %>% slice(1:50)
# converting data frame format
popular_res_names <- as.list(popular_res_names[,1])$res_name
data <- data[data$res_name %in% popular_res_names,]
dim(data)

## [1] 6789  412

Liczność ligandów według nazwy

count_by_name <- data %>% group_by(res_name) %>% summarise(cardinality = n()) %>% arrange(-cardinality)
count_by_name

## # A tibble: 50 x 2
##    res_name cardinality
##    <fct>          <int>
##  1 SO4             1007
##  2 GOL              632
##  3 EDO              516
##  4 NAG              464
##  5 CL               387
##  6 DMS              340
##  7 ZN               323
##  8 CA               284
##  9 HEM              260
## 10 MG               206
## # ... with 40 more rows

plot <- ggplot(count_by_name, aes(x = reorder(res_name, -cardinality), y = cardinality, fill = cardinality)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("ligand")+
  labs(title = "Liczność 50 najpopularniejszych ligandów według nazwy")

ggplotly(plot)